Video Games Sales Analysis¶

In this project , an interactive dashboard is made to visualize video games sales in different region over the past 5 decades.¶

In [1]:
import pandas as pd
In [15]:
df = pd.read_csv('vgchartz-2024.csv', parse_dates=['release_date']).rename({
    'title': 'Title',
    'console': 'Console',
    'genre': 'Genre',
    'publisher': 'Publisher',
    'developer': 'Developer'
}, axis=1).assign(release_year=lambda x:x['release_date'].dt.year)
In [16]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 64016 entries, 0 to 64015
Data columns (total 15 columns):
 #   Column        Non-Null Count  Dtype         
---  ------        --------------  -----         
 0   img           64016 non-null  object        
 1   Title         64016 non-null  object        
 2   Console       64016 non-null  object        
 3   Genre         64016 non-null  object        
 4   Publisher     64016 non-null  object        
 5   Developer     63999 non-null  object        
 6   critic_score  6678 non-null   float64       
 7   total_sales   18922 non-null  float64       
 8   na_sales      12637 non-null  float64       
 9   jp_sales      6726 non-null   float64       
 10  pal_sales     12824 non-null  float64       
 11  other_sales   15128 non-null  float64       
 12  release_date  56965 non-null  datetime64[ns]
 13  last_update   17879 non-null  object        
 14  release_year  56965 non-null  float64       
dtypes: datetime64[ns](1), float64(7), object(7)
memory usage: 7.3+ MB
In [17]:
df.describe()
Out[17]:
critic_score total_sales na_sales jp_sales pal_sales other_sales release_date release_year
count 6678.000000 18922.000000 12637.000000 6726.000000 12824.000000 15128.000000 56965 56965.000000
mean 7.220440 0.349113 0.264740 0.102281 0.149472 0.043041 2006-11-14 06:33:03.491617792 2006.359572
min 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1971-12-03 00:00:00 1971.000000
25% 6.400000 0.030000 0.050000 0.020000 0.010000 0.000000 2001-03-28 00:00:00 2001.000000
50% 7.500000 0.120000 0.120000 0.040000 0.040000 0.010000 2008-09-16 00:00:00 2008.000000
75% 8.300000 0.340000 0.280000 0.120000 0.140000 0.030000 2012-12-27 00:00:00 2012.000000
max 10.000000 20.320000 9.760000 2.130000 9.850000 3.120000 2024-12-31 00:00:00 2024.000000
std 1.457066 0.807462 0.494787 0.168811 0.392653 0.126643 NaN 8.617813
In [18]:
df.head(15)
Out[18]:
img Title Console Genre Publisher Developer critic_score total_sales na_sales jp_sales pal_sales other_sales release_date last_update release_year
0 /games/boxart/full_6510540AmericaFrontccc.jpg Grand Theft Auto V PS3 Action Rockstar Games Rockstar North 9.4 20.32 6.37 0.99 9.85 3.12 2013-09-17 NaN 2013.0
1 /games/boxart/full_5563178AmericaFrontccc.jpg Grand Theft Auto V PS4 Action Rockstar Games Rockstar North 9.7 19.39 6.06 0.60 9.71 3.02 2014-11-18 2018-01-03 2014.0
2 /games/boxart/827563ccc.jpg Grand Theft Auto: Vice City PS2 Action Rockstar Games Rockstar North 9.6 16.15 8.41 0.47 5.49 1.78 2002-10-28 NaN 2002.0
3 /games/boxart/full_9218923AmericaFrontccc.jpg Grand Theft Auto V X360 Action Rockstar Games Rockstar North NaN 15.86 9.06 0.06 5.33 1.42 2013-09-17 NaN 2013.0
4 /games/boxart/full_4990510AmericaFrontccc.jpg Call of Duty: Black Ops 3 PS4 Shooter Activision Treyarch 8.1 15.09 6.18 0.41 6.05 2.44 2015-11-06 2018-01-14 2015.0
5 /games/boxart/full_call-of-duty-modern-warfare... Call of Duty: Modern Warfare 3 X360 Shooter Activision Infinity Ward 8.7 14.82 9.07 0.13 4.29 1.33 2011-11-08 NaN 2011.0
6 /games/boxart/full_call-of-duty-black-ops_5Ame... Call of Duty: Black Ops X360 Shooter Activision Treyarch 8.8 14.74 9.76 0.11 3.73 1.14 2010-11-09 NaN 2010.0
7 /games/boxart/full_4653215AmericaFrontccc.jpg Red Dead Redemption 2 PS4 Action-Adventure Rockstar Games Rockstar Games 9.8 13.94 5.26 0.21 6.21 2.26 2018-10-26 2018-11-02 2018.0
8 /games/boxart/full_1977964AmericaFrontccc.jpg Call of Duty: Black Ops II X360 Shooter Activision Treyarch 8.4 13.86 8.27 0.07 4.32 1.20 2012-11-13 2018-04-07 2012.0
9 /games/boxart/full_4649679AmericaFrontccc.png Call of Duty: Black Ops II PS3 Shooter Activision Treyarch 8.0 13.80 4.99 0.65 5.88 2.28 2012-11-13 2018-04-07 2012.0
10 /games/boxart/full_809251AmericaFrontccc.jpg Call of Duty: Modern Warfare 2 X360 Shooter Activision Infinity Ward 9.5 13.53 8.54 0.08 3.63 1.28 2009-11-10 NaN 2009.0
11 /games/boxart/full_4380292AmericaFrontccc.jpg Call of Duty: WWII PS4 Shooter Activision Sledgehammer Games 8.1 13.40 4.67 0.40 6.21 2.12 2017-11-03 2017-12-31 2017.0
12 /games/boxart/full_call-of-duty-modern-warfare... Call of Duty: Modern Warfare 3 PS3 Shooter Activision Infinity Ward 8.8 13.35 5.54 0.49 5.78 1.54 2011-11-08 NaN 2011.0
13 /games/boxart/3570928ccc.jpg Grand Theft Auto III PS2 Action Rockstar Games DMA Design 9.5 13.10 6.99 0.30 4.51 1.30 2001-10-23 NaN 2001.0
14 /games/boxart/full_call-of-duty-black-ops_3Ame... Call of Duty: Black Ops PS3 Shooter Activision Treyarch 8.7 12.67 6.01 0.48 4.40 1.78 2010-11-09 NaN 2010.0
In [28]:
annual_sales=df.groupby('release_year').agg({'total_sales':'sum'}).reset_index()
In [29]:
annual_sales
Out[29]:
release_year total_sales
0 1971.0 0.00
1 1973.0 0.00
2 1975.0 0.00
3 1977.0 2.50
4 1978.0 2.36
5 1979.0 0.31
6 1980.0 2.26
7 1981.0 7.73
8 1982.0 28.99
9 1983.0 22.68
10 1984.0 4.85
11 1985.0 2.19
12 1986.0 10.35
13 1987.0 5.59
14 1988.0 8.73
15 1989.0 9.12
16 1990.0 9.07
17 1991.0 6.05
18 1992.0 9.38
19 1993.0 20.16
20 1994.0 36.85
21 1995.0 52.98
22 1996.0 101.36
23 1997.0 105.89
24 1998.0 132.23
25 1999.0 169.61
26 2000.0 171.12
27 2001.0 226.75
28 2002.0 314.50
29 2003.0 300.89
30 2004.0 284.68
31 2005.0 313.48
32 2006.0 244.42
33 2007.0 436.39
34 2008.0 538.11
35 2009.0 495.36
36 2010.0 454.02
37 2011.0 440.32
38 2012.0 285.47
39 2013.0 266.00
40 2014.0 292.11
41 2015.0 230.45
42 2016.0 208.02
43 2017.0 191.96
44 2018.0 148.03
45 2019.0 2.55
46 2020.0 3.45
47 2021.0 0.00
48 2022.0 0.00
49 2023.0 0.00
50 2024.0 0.00
In [25]:
import plotly.express as px
In [30]:
fig = px.line(annual_sales, x='release_year', y='total_sales', title="Annual Sales Over Years")
fig.show()
In [44]:
top10_titles=(df.groupby('Title').agg({'total_sales':'sum'})
              .reset_index()
              .sort_values(by='total_sales',ascending=False)
              .iloc[:10])
In [45]:
top10_titles
Out[45]:
Title total_sales
13724 Grand Theft Auto V 64.29
5266 Call of Duty: Black Ops 30.99
5281 Call of Duty: Modern Warfare 3 30.71
5273 Call of Duty: Black Ops II 29.59
5277 Call of Duty: Ghosts 28.80
5271 Call of Duty: Black Ops 3 26.72
5280 Call of Duty: Modern Warfare 2 25.02
20998 Minecraft 24.01
13719 Grand Theft Auto IV 22.53
5265 Call of Duty: Advanced Warfare 21.78
In [46]:
px.bar(top10_titles,x='Title',y='total_sales')
In [48]:
pip install dash

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In [50]:
pip install dash-bootstrap-templates

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In [51]:
from dash import Dash, dcc, html
from dash.dependencies import Output,Input
from dash_bootstrap_templates import load_figure_template
import dash_bootstrap_components as dbc
import numpy as np
In [76]:
# Load and preprocess data
video_games = (
    pd.read_csv('vgchartz-2024.csv', parse_dates=['release_date'])
    .rename({
        'title': 'Title',
        'console': 'Console',
        'genre': 'Genre',
        'publisher': 'Publisher',
        'developer': 'Developer'
    }, axis=1)
    .assign(release_year=lambda x: x['release_date'].dt.year)
)

dbc_css="https://cdn.jsdelivr.net/gh/AnnMarieW/dash-bootstrap-templates/dbc.min.css"

# Initialize Dash app
app = Dash(__name__,external_stylesheets=[dbc.themes.VAPOR,dbc_css])
load_figure_template("VAPOR")

# Define layout
app.layout = dbc.Container([
    html.H1("Video Game Explorer", style={"text-align": "center"}),
    
    # Category selection row
    dbc.Row([
        dbc.Col([
            dbc.Card([
                dcc.Markdown("**Select a Category**"),
                dcc.Dropdown(
                    id='category-dropdown',
                    options=[
                        {"label": "Title", "value": "Title"},
                        {"label": "Genre", "value": "Genre"},
                        {"label": "Publisher", "value": "Publisher"},
                        {"label": "Developer", "value": "Developer"},
                        {"label": "Console", "value": "Console"},
                    ],
                    value="Title",
                    className="dbc"
                )
            ])
        ])
    ], className="mb-3"),
    
    # Region selection row
    dbc.Row([
        dbc.Col([
            dbc.Card([
                dcc.Markdown("**Select a Region**"),
                dcc.RadioItems(
                    id='region-radio',
                    options=[
                        {"label": "World Total", "value": "total_sales"},
                        {"label": "North America", "value": "na_sales"},
                        {"label": "Japan", "value": "jp_sales"},
                        {"label": "Europe/Africa", "value": "pal_sales"},
                        {"label": "Rest of World", "value": "other_sales"},
                    ],
                    value="total_sales",
                    className="dbc"
                )
            ])
        ])
    ], className="mb-3"),
   html.Br(),
   dbc.Row(dcc.Graph(id='sales_line')),
   html.Br(),
   dbc.Row(dcc.Graph(id='rankings_bar')),
])
@app.callback(
     Output("sales_line","figure"),
     Output("rankings_bar","figure"),
     Input("category-dropdown","value"),
     Input("region-radio","value"),
)

def vg_plotter(category,region):
    
    annual_sales=video_games.groupby("release_year",as_index=False).agg({region:"sum"})
    
    fig=px.line(
           annual_sales, 
           x='release_year', 
           y=region, title=f"Video Game Sales in {region} Over Time").update_layout(title_x=0.5)
    
    top10_sellers=(video_games.groupby(category,as_index=False).agg({region:'sum'})
              .reset_index()
              .sort_values(region,ascending=False)
              .iloc[:10])
    fig2=px.bar(
            top10_sellers,
            x=category,
            y=region,
            title=f"Top Video Game Sales by Category").update_layout(title_x=0.5)
    
    return fig,fig2
    

# Run server
if __name__ == "__main__":
    app.run_server(debug=True)
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